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Description: In modern era of information age, digitalization has revolutionized like never before. Powerful computers, advanced photo editing software packages and high resolution capturing devices have made m...

In modern era of information age, digitalization has revolutionized like never before. Powerful computers, advanced photo editing software packages and high resolution capturing devices have made manipulation of digital images incredibly easy. As per as image forensics concerns, one of the most actively researched area are detection of copy move forgeries. Higher computational complexity is one of the major component of existing techniques to detect such tampering. Moreover, copy move forgery is usually performed in three steps. First, copying of a region in an image then pasting the same one in the same respective image and finally doing some post-processing like rotation, scaling, shift, noise, etc. Consequently, pseudo Zernike moment is used as a features extraction method for matching image blocks and as a primary factors on which performance of detection algorithms depends.

IJSRD - International Journal for Scientific Research & Development| Vol.

4, Issue 04, 2016 | ISSN (online): 2321-0613

Copy Move Image Forgery Detection using Pseudo Zernike Moment for
Better Detection Accuracy
Grishma Solanki1 Mr. Karshan Kandoriya2
1
P.G. Student 2Assistant Professor
1
Department of Information Technology 2Department of Computer Science & Engineering
1,2
Parul Institute of Engineering and Technology, Vadodara, Gujarat, India
Abstract— In modern era of information age, digitalization Image forgery can be broadly classified in three
has revolutionized like never before. Powerful computers, categories namely image forgery using splicing, copy move
advanced photo editing software packages and high image forgery and image resampling [2]. This paper focuses
resolution capturing devices have made manipulation of on copy-move image forgery and its detection methods. The
digital images incredibly easy. As per as image forensics paper is organized as follows. In section-II, related work is
concerns, one of the most actively researched area are discussed. In section-III, copy-move image forgery and its
detection of copy move forgeries. Higher computational detection techniques are discussed. Section-IV proposed
complexity is one of the major component of existing method. In section-V Implementation of proposed method
techniques to detect such tampering. Moreover, copy move and section-VI presents conclusion and future enhancement.
forgery is usually performed in three steps. First, copying of
a region in an image then pasting the same one in the same II. RELATED WORK
respective image and finally doing some post-processing like In digital images, there are many passive detection
rotation, scaling, shift, noise, etc. Consequently, pseudo techniques have been proposed to detect image forgery. We
Zernike moment is used as a features extraction method for can use block based or key point based matching methods
matching image blocks and as a primary factors on which for detection of forgery and the performance of the detection
performance of detection algorithms depends. algorithms depends mainly on the features, which are used
Key words: Image, Image Forensics, Image Forgery, Copy- for matching the blocks [7].
Move Image Forgery Osamah M. Al-Qershi and Khoo Bee Ee discussed
different methods of image forgery detection in digital
I. INTRODUCTION image forensics. The problem of authentication of an images
In our daily life digital media is playing a vital role because are addressed by digital image forensics [5].
of popularity of low cost and high resolution cameras. As Hoda Marouf and Karim Faez proposed new
the image processing software have been developed, even efficient facial-based identical twins recognition according
people who are not experts in image processing can easily to the geometric moment. Pseudo zernike moment is robust
alter digital images. It brings about great benefits, but also to rotation, scaling and shift. Also, the facial area inside an
side effects: a number of tampered images have recently image is detected using Ada Boost approach [6].
been distributed or have even been published by major In one paper Osamah M. Al-Qershi and Bee Ee
newspapers. Therefore, verification of authenticity is Khoo proposed an enhanced matching method for copy
important for digital images. Among different forgery move image forgery detection using Zernike moment. As
techniques using typical image processing tools, copy-move Zernike moment is invariant to rotation and in some cases
forgery is one of the most commonly used methods. The invariant to scaling also. It provide better detection accuracy
copy-move forgery copies a part of the image and pastes it compared to other passive techniques [7].
into another part of the image to conceal an evidence or In one paper authors uses Zernike moment for
deceive people. Figure 1 shows an example of the altered detection of object which are copied moved or rotated. By
photograph released by Iran and published by western media using Zernike moment the ratio of false positive can be less
including The New York Times, The Los Angeles Times, [8].
BBC News and etc. on July 9, 2008[1].
In fig. 1(a), two major sections (encircled in black) III. COPY-MOVE IMAGE FORGERY DETECTION
appear to be replicated from other sections (encircled in Copy-move is an image forgery technique in which parts of
white). Actually figure 1(a) was released on the front pages an original image, after some possible geometric and
of those of newspapers and lately corrected to the original illumination adjustments, are copied, moved to a desired
image as figure 1(b). location in the same image and pasted (e.g. refer figure 1).
The main aim of copy-move image forgery is to hide certain
details or to duplicate some aspects of an image [3].
Generally, Copy-Move forgery detection techniques can be
classified into two: Block based approaches and Key-point
based approaches. In both the approaches some form of pre-
processing will be there. In block based methods, the image
will be divided into overlapping blocks of specified size and
a feature vector will be computed for these blocks. Similar
Fig. 1: An example of copy-move forgery [1]: (a) the forged feature vectors are then matched to find the forged regions.
image with four missiles and (b) the original image with In Key-point based methods, feature vectors are computed
three missiles for regions with high entropy [4]. There is no subdivision

into blocks. The feature vectors are matched to find the noise, rotation, scaling. But Zernike moment does not
copied blocks. invariant to shift and affine transformation. So we are using
pseudo Zernike moment as a feature extraction method that
IV. PROPOSED METHOD is invariant to rotation, scaling, shift and affine
In this section pseudo Zernike moment is used as a feature transformation and it is also faster than Zernike moment. So
extraction method for better detection accuracy and to it can improve accuracy and reduce computational overhead.
overcome limitations of existing Zernike moment method. So in future work we can use pseudo Zernike moment as a
Proposed method work well against objects that are feature extraction method in video image forgery. So we can
rotated, scaled and shifted. In optical systems pseudo- provide more security in video like in video surveillance
zernike polynomials are widely used. It is also widely used system.
in image analysis and shape descriptors. Pseudo Zernike
moment uses global information in an image for extracting REFERENCES
features. [1] In an Iranian image, a missile too many,
https://thelede.blogs.nytimes.com/2008/07/10/in-an-
iranian-image-a-missile-too-many/
[2] Qureshi M. Ali and Deriche M., “A Review on Copy
Move Image Forgery Detection Techniques”, Feb.
2014.
[3] Kudke Swapnil H. and Gawande A. D., “Copy- Move
Attack Forgery Detection by Using SIFT”, International
Journal of Innovative Technology and Exploring
Engineering (IJITEE), Apr 2013.
[4] Harpreet Kaur, Jyoti Saxena and Sukhjinder Singh,
“Key-point based copy-move forgery detection and
their hybrid methods: A Review”, June 2015.
[5] Osamah M. Al-Qershi and Khoo Bee Ee, “Passive
Detection of Copy-Move Forgery in Digital Images:
State-of-the-art” 2013.
[6] Hoda Marouf and Karim Faez, “An efficient feature
extraction method with pseudo Zernike moment for
Fig. 2: Flowchart of proposed work facial recognition of identical twins” 2014.
[7] Osamah M. Al-Qershi and Bee Ee Khoo, “Enhanced
V. IMPLEMENTATION Matching Method for Copy-Move Forgery Detection by
For implementation MATLAB environment is used to run Means of Zernike Moments” Springer International
pseudo Zernike moment. Some of results from proposed Publishing Switzerland 2015.
method is mentioned here. [8] Seung-Jin Ryu, Min-Jeong Lee, and Heung-Kyu Lee,
Below graph shows the comparison between “Detection of Copy-Rotate-Move Forgery Using
Zernike and pseudo Zernike moment detection accuracy. Zernike Moments” 2010.

Fig. 3: Comparison graph of both methods

VI. CONCLUSION AND FUTURE ENHANCEMENT
From the recent surveyed methods on copy move forgery
detection we can conclude that by using block based or
matching methods with feature extraction we can reduce
some intermediate and post processing operations like,